Spaces:
Paused
Paused
File size: 3,824 Bytes
78db0f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from .cv_ox_det import inference_detector
from .cv_ox_pose import inference_pose
from typing import List, Optional
from .types import PoseResult, BodyResult, Keypoint
class Wholebody:
def __init__(self, onnx_det: str, onnx_pose: str):
# Always loads to CPU to avoid building OpenCV.
device = 'cpu'
backend = cv2.dnn.DNN_BACKEND_OPENCV if device == 'cpu' else cv2.dnn.DNN_BACKEND_CUDA
# You need to manually build OpenCV through cmake to work with your GPU.
providers = cv2.dnn.DNN_TARGET_CPU if device == 'cpu' else cv2.dnn.DNN_TARGET_CUDA
self.session_det = cv2.dnn.readNetFromONNX(onnx_det)
self.session_det.setPreferableBackend(backend)
self.session_det.setPreferableTarget(providers)
self.session_pose = cv2.dnn.readNetFromONNX(onnx_pose)
self.session_pose.setPreferableBackend(backend)
self.session_pose.setPreferableTarget(providers)
def __call__(self, oriImg) -> Optional[np.ndarray]:
det_result = inference_detector(self.session_det, oriImg)
if det_result is None:
return None
keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
keypoints_info = np.concatenate(
(keypoints, scores[..., None]), axis=-1)
# compute neck joint
neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
# neck score when visualizing pred
neck[:, 2:4] = np.logical_and(
keypoints_info[:, 5, 2:4] > 0.3,
keypoints_info[:, 6, 2:4] > 0.3).astype(int)
new_keypoints_info = np.insert(
keypoints_info, 17, neck, axis=1)
mmpose_idx = [
17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3
]
openpose_idx = [
1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17
]
new_keypoints_info[:, openpose_idx] = \
new_keypoints_info[:, mmpose_idx]
keypoints_info = new_keypoints_info
return keypoints_info
@staticmethod
def format_result(keypoints_info: Optional[np.ndarray]) -> List[PoseResult]:
def format_keypoint_part(
part: np.ndarray,
) -> Optional[List[Optional[Keypoint]]]:
keypoints = [
Keypoint(x, y, score, i) if score >= 0.3 else None
for i, (x, y, score) in enumerate(part)
]
return (
None if all(keypoint is None for keypoint in keypoints) else keypoints
)
def total_score(keypoints: Optional[List[Optional[Keypoint]]]) -> float:
return (
sum(keypoint.score for keypoint in keypoints if keypoint is not None)
if keypoints is not None
else 0.0
)
pose_results = []
if keypoints_info is None:
return pose_results
for instance in keypoints_info:
body_keypoints = format_keypoint_part(instance[:18]) or ([None] * 18)
left_hand = format_keypoint_part(instance[92:113])
right_hand = format_keypoint_part(instance[113:134])
face = format_keypoint_part(instance[24:92])
# Openpose face consists of 70 points in total, while DWPose only
# provides 68 points. Padding the last 2 points.
if face is not None:
# left eye
face.append(body_keypoints[14])
# right eye
face.append(body_keypoints[15])
body = BodyResult(
body_keypoints, total_score(body_keypoints), len(body_keypoints)
)
pose_results.append(PoseResult(body, left_hand, right_hand, face))
return pose_results
|